
class 							lowerCAmelCase__   :

						def __init__(      self							,				a							,				a					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						name
								_UpperCamelCase         =						val

						def __str__(      self					)       ->   Tuple:


								'''simple docstring'''

								return F'{self.__class__.__name__}({self.name}, {self.val})'







						def __lt__(      self							,				a					)       ->   Tuple:


								'''simple docstring'''

								return self.val < other.val







class 							lowerCAmelCase__   :

						def __init__(      self							,				a					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						{}
								_UpperCamelCase         =						{}
								_UpperCamelCase         =						self.build_heap(a_					)

						def __getitem__(      self							,				a					)       ->   str:


								'''simple docstring'''

								return self.get_value(a_					)

						def 			A_					(      self							,				a					)       ->   Union[str, Any]:


								'''simple docstring'''

								return (idx - 1) // 2

						def 			A_					(      self							,				a					)       ->   Dict:


								'''simple docstring'''

								return idx * 2 + 1

						def 			A_					(      self							,				a					)       ->   str:


								'''simple docstring'''

								return idx * 2 + 2

						def 			A_					(      self							,				a					)       ->   Tuple:


								'''simple docstring'''

								return self.heap_dict[key]

						def 			A_					(      self							,				a					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						len(a_					) - 1
								_UpperCamelCase         =						self.get_parent_idx(a_					)

								for idx, i in enumerate(a_					):
										_UpperCamelCase         =						idx
										_UpperCamelCase         =						i.val

								for i in range(a_							,				-1							,				-1					):
										self.sift_down(a_							,				a_					)
								return array

						def 			A_					(      self							,				a							,				a					)       ->   str:


								'''simple docstring'''

								while True:
										_UpperCamelCase         =						self.get_left_child_idx(a_					)  # noqa: E741
										_UpperCamelCase         =						self.get_right_child_idx(a_					)

										_UpperCamelCase         =						idx
										if l < len(a_					) and array[l] < array[idx]:
												_UpperCamelCase         =						l
										if r < len(a_					) and array[r] < array[smallest]:
												_UpperCamelCase         =						r

										if smallest != idx:
												_UpperCamelCase         =						array[smallest], array[idx]
												(
												    _UpperCamelCase		
												)         =						(
												    self.idx_of_element[array[smallest]],
												    self.idx_of_element[array[idx]],
												)
												_UpperCamelCase         =						smallest
										else:
												break

						def 			A_					(      self							,				a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_parent_idx(a_					)
								while p >= 0 and self.heap[p] > self.heap[idx]:
										_UpperCamelCase         =						self.heap[idx], self.heap[p]
										_UpperCamelCase         =						(
										    self.idx_of_element[self.heap[idx]],
										    self.idx_of_element[self.heap[p]],
										)
										_UpperCamelCase         =						p
										_UpperCamelCase         =						self.get_parent_idx(a_					)

						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								return self.heap[0]

						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						self.heap[-1], self.heap[0]
								_UpperCamelCase         =						(
								    self.idx_of_element[self.heap[-1]],
								    self.idx_of_element[self.heap[0]],
								)

								_UpperCamelCase         =						self.heap.pop()
								del self.idx_of_element[x]
								self.sift_down(0							,				self.heap					)
								return x

						def 			A_					(      self							,				a					)       ->   int:


								'''simple docstring'''

								self.heap.append(a_					)
								_UpperCamelCase         =						len(self.heap					) - 1
								_UpperCamelCase         =						node.val
								self.sift_up(len(self.heap					) - 1					)

						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								return len(self.heap					) == 0







						def 			A_					(      self							,				a							,				a					)       ->   int:


								'''simple docstring'''

								assert (
								    self.heap[self.idx_of_element[node]].val > new_value
								), "newValue must be less that current value"
								_UpperCamelCase         =						new_value
								_UpperCamelCase         =						new_value
								self.sift_up(self.idx_of_element[node]					)




lowerCamelCase__						=				Node("R", -1)
lowerCamelCase__						=				Node("B", 6)
lowerCamelCase__						=				Node("A", 3)
lowerCamelCase__						=				Node("X", 1)
lowerCamelCase__						=				Node("E", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCamelCase__						=				MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
		print(i)

print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
		print(i)

if __name__ == "__main__":
		import doctest

		doctest.testmod()










'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		lowerCamelCase	(   _A							):
	snake_case_   = ["image_processor", "tokenizer"]
	snake_case_   = "LayoutLMv3ImageProcessor"
	snake_case_   = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



	def __init__( self		,   a_=None		,   a_=None		,   **a_						):
			lowerCAmelCase  :     Union[str, Any]       						= None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,   a_		,   )
					lowerCAmelCase  :     Tuple       						= kwargs.pop("feature_extractor"						)

			lowerCAmelCase  :     str       						= image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError("You need to specify an `image_processor`."						)
			if tokenizer is None:
					raise ValueError("You need to specify a `tokenizer`."						)

			super().__init__(a_		,   a_						)



	def __call__( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = True		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = 0		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = True		,   a_ = None		,   **a_		,   ):
			# verify input
			if self.image_processor.apply_ocr and (boxes is not None):
					raise ValueError(
					    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

			if self.image_processor.apply_ocr and (word_labels is not None):
					raise ValueError(
					    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

			# first, apply the image processor
			lowerCAmelCase  :     str       						= self.image_processor(images=a_		,   return_tensors=a_						)

			# second, apply the tokenizer
			if text is not None and self.image_processor.apply_ocr and text_pair is None:
					if isinstance(a_		,   a_						):
							lowerCAmelCase  :     Union[str, Any]       						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
					lowerCAmelCase  :     int       						= features["words"]

			lowerCAmelCase  :     List[Any]       						= self.tokenizer(
			    text=text if text is not None else features["words"]		,   text_pair=text_pair if text_pair is not None else None		,   boxes=boxes if boxes is not None else features["boxes"]		,   word_labels=a_		,   add_special_tokens=a_		,   padding=a_		,   truncation=a_		,   max_length=a_		,   stride=a_		,   pad_to_multiple_of=a_		,   return_token_type_ids=a_		,   return_attention_mask=a_		,   return_overflowing_tokens=a_		,   return_special_tokens_mask=a_		,   return_offsets_mapping=a_		,   return_length=a_		,   verbose=a_		,   return_tensors=a_		,   **a_		,   )

			# add pixel values
			lowerCAmelCase  :     Tuple       						= features.pop("pixel_values"						)
			if return_overflowing_tokens is True:
					lowerCAmelCase  :     Optional[int]       						= self.get_overflowing_images(a_		,   encoded_inputs["overflow_to_sample_mapping"]						)
			lowerCAmelCase  :     str       						= images

			return encoded_inputs



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
			lowerCAmelCase  :     Optional[Any]       						= []
			for sample_idx in overflow_to_sample_mapping:
					images_with_overflow.append(images[sample_idx]						)

			if len(a_						) != len(a_						):
					raise ValueError(
					    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
					    F''' {len(a_						)} and {len(a_						)}'''						)

			return images_with_overflow



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.batch_decode(*a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.decode(*a_		,   **a_						)



	@property
	def 	_lowerCamelCase     ( self						):
			return ["input_ids", "bbox", "attention_mask", "pixel_values"]



	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,   a_		,   )
			return self.image_processor_class





	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,   a_		,   )
			return self.image_processor



